Essential tremor plus rest tremor: current concepts and controversies

Distinguishing essential tremor from Parkinson's disease: clinical and experimental tools

INTRODUCTION

Essential tremor (ET) and Parkinson's disease (PD) are the most common causes of tremor and the most prevalent movement disorders, with overlapping clinical features that can lead to diagnostic challenges, especially in the early stages.

AREAS COVERED

In the present paper, the authors review the clinical and experimental studies and emphasized the major aspects to differentiate between ET and PD, with particular attention to cardinal phenomenological features of these two conditions. Ancillary and experimental techniques, including neurophysiology, neuroimaging, fluid biomarker evaluation, and innovative methods, are also discussed for their role in differential diagnosis between ET and PD. Special attention is given to investigations and tools applicable in the early stages of the diseases, when the differential diagnosis between the two conditions is more challenging. Furthermore, the authors discuss knowledge gaps and unsolved issues in the field.

EXPERT OPINION

Distinguishing ET and PD is crucial for prognostic purposes and appropriate treatment. Additionally, accurate diagnosis is critical for optimizing clinical and experimental research on pathophysiology and innovative therapies. In a few years, integrated technologies could enable accurate, reliable diagnosis from early disease stages or prodromal stages in at-risk populations, but further research combining different techniques is needed.

Testing for Alcohol Responsiveness in Familial Essential Tremor

Background

Essential tremor (ET) is the most common movement disorder in adults and is considered to be highly heritable. A reduction of the tremor amplitude after alcohol consumption is reported in approximately half of the patients. In this study, we describe the alcohol response in our familial ET cohort by employing an alcohol responsivity test designed by Knudsen et al. outside its original research group for the first time.

Methods

We recruited families with at least three trembling family members and confirmed ET diagnoses. During the in-hospital alcohol responsivity test, tremor was measured using Archimedes spirals before alcohol consumption (T0), one hour after alcohol intake (T1), and the next morning (T2). The spirals were rated by two independent raters using the Bain Findley scale. The average of these two scores was calculated as the Archimedes Spiral Rating (ASR) for each time point.

Results

Twenty-four confirmed ET patients were included for analysis. The median ASR at T0 (5.0) and T2 (4.75) were significantly higher than the median ASR at T1 (3.25) (both p < 0.001). In 67% of patients, a difference in ASR between T0 and T1 (dASR) ≥ 2 pointed towards an improvement of tremor after consuming alcohol.

Discussion

We confirmed that the alcohol responsiveness test of Knudsen et al. is useful in determining objective alcohol responsivity. We established a significantly reduced ASR after alcohol consumption in 67% of familial ET patients in our cohort. In the future, a larger population is needed to establish whether familial aggregation of alcohol responsivity occurs in essential tremor patients.

Highlights

The test designed by Knudsen et al. effectively established objective alcohol responsiveness outside its original research group.We found an objective alcohol response in 67% of our familial ET cohort.Subjective VAS scores were significantly lower after alcohol consumption.There was no correlation between the objective and subjective alcohol responsiveness.Familial aggregation of alcohol responsiveness in ET should be studied in a larger cohort.

Clinical and kinematic characterization of parkinsonian soft signs in essential tremor

BACKGROUND

Subtle parkinsonian signs, i.e., rest tremor and bradykinesia, are considered soft signs for defining essential tremor (ET) plus.

OBJECTIVES

Our study aimed to further characterize subtle parkinsonian signs in a relatively large sample of ET patients from a clinical and neurophysiological perspective.

METHODS

We employed clinical scales and kinematic techniques to assess a sample of 82 ET patients. Eighty healthy controls matched for gender and age were also included. The primary focus of our study was to conduct a comparative analysis of ET patients (without any soft signs) and ET-plus patients with rest tremor and/or bradykinesia. Additionally, we investigated the asymmetry and side concordance of these soft signs.

RESULTS

In ET-plus patients with parkinsonian soft signs (56.10% of the sample), rest tremor was clinically observed in 41.30% of cases, bradykinesia in 30.43%, and rest tremor plus bradykinesia in 28.26%. Patients with rest tremor had more severe and widespread action tremor than other patients. Furthermore, we observed a positive correlation between the amplitude of action and rest tremor. Most ET-plus patients had an asymmetry of rest tremor and bradykinesia. There was no side concordance between these soft signs, as confirmed through both clinical examination and kinematic evaluation.

CONCLUSIONS

Rest tremor and bradykinesia are frequently observed in ET and are often asymmetric but not concordant. Our findings provide a better insight into the phenomenology of ET and suggest that the parkinsonian soft signs (rest tremor and bradykinesia) in ET-plus may originate from distinct pathophysiological mechanisms.

Bedside clinical assessment of patients with common upper limb tremor and algorithmic approach

The diagnostic approach for patients with tremor is challenging due to the complex and overlapping phenotypes among tremor syndromes. The first step in the evaluation of tremor is to identify the tremulous movement and exclude the tremor mimics. The second step is to classify the tremor syndrome based on the characteristics of tremor from historical clues and focused examination (Axis 1). Comprehensive tremor examinations involve the assessment of tremor in different conditions (rest, action or mixed, position or task-specific), distribution of tremor (upper limb, lower limb, head, jaw), positive signs for functional tremor (FT) if suspected (distractibility, entrainment, co-contraction), and associated neurological signs including parkinsonism, dystonic posture, cerebellar/brainstem signs, neuropathy, and cognitive impairment. A pivotal feature in this step is to determine any distinct feature of a specific isolated or combined tremor syndrome. In this review, we propose an algorithm to assess upper limb tremors. Ancillary testing should be performed if clinical evaluation is unclear. The choice of investigation depends on the types of tremors considered to narrow down the spectrum of etiology (Axis 2). Laboratory blood tests are considered for acute onset and acute worsening of tremors, while structural neuroimaging is indicated in unilateral tremors with acute onset, nonclassical presentations, and a combination of neurological symptoms. Neurophysiological study is an important tool that aids in distinguishing between tremor and myoclonus, etiology of tremor and document specific signs of FT. Treatment is mainly symptomatic based depending on the etiology of the tremor and the patient's disabilities.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor

Objective

To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET.

Methods

We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients.

Results

ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms.

Conclusion

This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Cortical involvement in essential tremor with and without rest tremor: a machine learning study

INTRODUCTION

There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes.

METHODS

Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients.

RESULTS

rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups.

CONCLUSION

Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.